Time-series datasets are generated in a wide variety of circumstances, and can be used for analysis and control. One broad arena which is expected to generate vast amounts of time-series data is the so-called Internet-of-Things (IoT), in which large numbers of devices of many disparate sorts are provided with network connectivity to provide monitoring or control. In many examples, position data can be included in time-series datasets (e.g., geo-coordinates such as latitude and longitude, perhaps elevation or altitude as well). A few examples (by no means exhaustive) of areas in which time-series datasets might be generated and exploited include the following, some of which overlap with one another.
In so-called Connected Transportation, time-series datasets can include vital statistics or operating parameters for automobiles, trucks, trains (or individual locomotives or rail cars), aircraft, boats, and ships, and so forth. Those time-series datasets can be analyzed for predictive diagnosis, scheduling maintenance, failure predication or analysis, accident investigation and analysis, and the like. Position coordinates included in a time-series dataset can be employed for vehicle navigation, traffic flow management, fleet or asset management, personnel management, scheduling, and so forth.
In so-called Smart Cities, time-series datasets can include parking availability, traffic patterns, monitoring of buildings, roads, bridges, powerlines and power grids, telecommunications grids, water and sewer lines, gas lines, or other infrastructure, water quality monitoring, noise levels, lighting conditions and resources, trash or garbage accumulation, and so forth. Those time-series datasets can be employed to monitoring conditions for incident management, maintenance scheduling, load balancing, failure warning or prediction, leak-detection, optimized weather- and time-dependent street lighting, refuse pickup, usage analysis, and so forth. Position coordinates can be advantageously included in some of those time-series datasets.
In so-called Smart Environment, time-series datasets can include forest weather conditions (temperature, humidity, cloud conditions, precipitation), conditions, soil moisture, rainfall monitoring, waterway flow rates or water levels, flood prediction or monitoring, snowpack levels, avalanche conditions, landslide conditions, seismic monitoring, combustion gas monitoring, pollen levels, airborne or waterborne levels of CO2, methane, other hydrocarbons, or other volatile organic compounds (VOCs), sulfur or nitrogen oxides, soot or other particulates, ozone, or other pollutants, and so forth. Those time-series datasets can be employed to for planning, analyzing, or evaluating in order to provide a variety of warnings, management, remediation, mitigation, or other functions. Position coordinates can be advantageously included in some of those time-series datasets.
In industrial settings, time-series datasets can include operational parameters, equipment or machinery conditions or operation, tank, storage, pipeline, or supply line monitoring (oil, gas, water, chemical feedstocks, etc.), leak or spill detection, mitigation, or remediation (especially explosive, combustible, toxic, or radioactive materials), power generation (coal, natural gas, nuclear, solar, wind), airborne or waterborne levels of CO2, methane, other hydrocarbons, or other volatile organic compounds (VOCs), sulfur or nitrogen oxides, soot or other particulates, ozone, or other pollutants, leakage from a water line or a roof/window leak, corrosion detection, and so forth. Position coordinates can be advantageously included in some of those time-series datasets.
In retail or logistics settings, time-series datasets can include product location (warehouse, retail outlet, in-transit, etc.), product rotation or disposal, supply chain monitoring or control, restocking, monitoring shipments (location, handling, vibration, cold chain maintenance, container openings, and so on), location or contents of specific truck trailers, rail cars, or shipping containers, asset monitoring (via RFID tags, barcodes, and the like), fleet or personnel management, and so forth. Position coordinates can be advantageously included in some of those time-series datasets.
In agricultural or animal husbandry settings, time-series datasets can include rainfall and soil moisture monitoring, weather monitoring, soil chemistry, pH, or microbial conditions, green house temperature and humidity, hydroponics conditions, micro-climate control, temperature and humidity control of crop, grain, hay, straw, alfalfa storage, irrigation control or monitoring, location, identification, fertility, or health of livestock, and so forth. Position coordinates can be advantageously included in some of those time-series datasets.
In health care settings, time-series datasets can include patient data (historical or nearly real-time) such as height, weight, blood pressure, heart rate, blood chemistry, blood oxygenation, and so on, fall detection, patient surveillance (in hospital or other facility or at home), medical or surgical history, and so on.